Bioinformatics is a modern information technology for computerized warehousing, visualization and analysis of biological data. The technology emerged in response to the explosive growth of biological information, in forms such as whole-genome sequences, whole-genome expression patterns and high-throughput structure characterization of proteins. One form of biological information, namely the multi-species interactions of a biological system, is of a higher-level complexity that naturally requires bioinformatics solutions. Development of bioinformatics systems of species interactions, however, is in its infancy compared with the proliferation of computing systems for genomics and proteomics. Lyme disease, the most prevalent vector-borne disease in the United States, is a model system for the study of species interactions between a pathogen (Borrelia burgdorferi), its vector (Ixodes ticks) and hosts (vertebrates). In this application, we propose to develop a database system for the study of biogeographic variations of Lyme disease as a multi-species infectious system. The database will host multiple Borrelia genomes, multi-locus sequence variations of Borrelia and Ixodes, as well as biogeographic and ecological information associated with the genome and sequence information. Genomes, sequence variations and biogeographic information will be collected from public databases and published studies. Tools for evolutionary analyses, such as phylogenetic reconstruction and tests of neutral evolution of sequence variations, will be developed and integrated with the database system. In addition, we will implement and curate a web interface to the database system. There are two specific aims for the proposed pilot project. First, we will develop a proof-of-concept database of Lyme disease biogeography, complete with Borrelia genome information, Borrelia and tick multi-locus genetic variations and ecological data on Borrelia-Tick-Host associations. Secondly, we will validate the utility of the database system by testing two evolutionary hypotheses, one on the co-evolutionary history of Borrelia and ticks, and the other on the Borrelia adaptive divergence between geographic localities. The project will pave the way for a bioinformatics system for automated analyses and visualization of biogeographic patterns of vector-borne infectious systems.